Streamlining veterinary practice
Integration of clinical data
Training Artificial Intelligences
We support veterinarians
in case management, during medical treatment, in the analysis of health data, and in communication with owners.
Veterinarians must manage a tremendous amount of data in a short time in their daily clinical practice. Artificial Intelligence has the potential to disburden veterinarians.
With the prospect that in the future they can better focus on tasks that ultimately only humans can handle: communicating, prioritising, deciding, and treating.
Medicine is, to a significant extent, the ability to analyse data and draw conclusions for concrete actions.
In pattern recognition, medical professionals can leverage the tirelessness of machines.
- Facilitation of communication between veterinarians and pet owners
- Focused exchange
- Facilitation of veterinary training
- Compensation for the shortage of professionals in rural areas
- Improvement of service documentation
- Facilitation of knowledge transfer between research and practice
- Animal welfare
- Facilitation of professional communication between veterinarians
- Automation of documentation
- Improvement of medical documentation
- Reduction of barriers to contacting the veterinarian
- Improvement of the data basis for research
- Increase in scientific output
- Improvement of education
- Acceleration of epidemiological data collection
- Early detection of diseases
- Prevention
- Assistance in medical tasks
Artificial Intelligence is a catalyst for advancing veterinary medicine. To harness the potentials of Artificial Intelligences, we need to focus on three things.
Streamlining veterinary practice
Facilitation means organisation. Before one can hope for facilitation through new technologies, the veterinary daily routine must be organised and systematised.
We simplify documentation, reduce communication pathways, establish professional case management, optimise onboarding and training, provide state of the art protocols, automate the generation of medical reports, and organise the relationship between veterinarians and owners.
Integration of clinical data
Training Artificial Intelligences is only possible based on organised, complete, and sufficient data.
Veterinary medicine is increasingly evolving into an evidence-based science. The statistical populations on which state of the art is based must be expanded. We see it as our task to connect scientific data from everyday veterinary medicine, university and non-university research, and long-term monitoring in aftercare and prevention. Through impartial moderation of anonymised scientific data and monitoring safety standards, we pave the way for collective intelligence.
Training Artificial Intelligences
We are developing pattern recognition procedures. These will translate into useful tools for diagnostics and prognosis.
We encounter complex systems in our environment daily and naturally use our human intelligence to navigate them. The use of Artificial Intelligence involves deploying programs to solve problems such as pattern recognition in such complex environments. Through machine learning, we train Artificial Intelligences with examples from which they can draw conclusions based on recurring patterns.
Artificial Intelligences already deliver impressive results, yet they are still in their early stages. Artificial Intelligences will evolve into a significant tool for veterinarians. Not less, but also not more. Crucial for long-term integration will be using Artificial Intelligences not to drive a wedge between veterinarians and pet owners. While Artificial Intelligence can become a profitable business model, it must fundamentally serve the public good.
animalrecords
anirec
anirec
anirec
is our app
for veterinarians and owners.
It works on both mobile phones and desktops. It integrates telemedicine, case management, and Artificial Intelligence. It streamlines veterinary practice, processes health data, and provides the veterinarian with AI-assisted initial opinions in diagnosis.
anirec facilitates and focuses the communication with owners during treatment, follow-up care, and early detection.
Version 1.1.1
June 2024
The app is currently in the pilot phase.
anirec
our agenda
We are working to make it significantly easier for researchers to collect long-term data and thereby evaluate treatment methods for their effectiveness. Simplification of long-term studies - 2022
We are already training an Artificial Intelligence capable of diagnosing over 14 diseases of the equine eye. Diagnosis of over 14 diseases of the horse's eye - 2023
We are developing a tool that allows every veterinarian and clinic to measure the success rates of their treatments statistically correct, thereby contributing to evidence-based medicine. Statistics of their own work - 2024
We aim for young veterinarians to be able to adhere to state of the art examination protocols, regardless of the quality of their training. State of the art protocols - 2024
It is of significant concern to us to facilitate communication between veterinarians and owners, so that owners do not unnecessarily worry and veterinarians can focus on treatment. Improved communication between veterinarian and owner - 2025
We aim to provide a practical method that allows owners to easily and safely implement veterinary treatment instructions. Understandable instructions for owners - 2025
We want to ensure that at the beginning of a treatment, the veterinarian receives a comprehensive preliminary report that demands as little time as possible from all parties involved. Preliminary report without stress - 2025
We are working on making our app indicate whether a horse is expressing acute pain and the intensity of these expressions. Pain Score - 2026
We ensure that creating a meaningful and understandable medical report is accomplished within seconds. Ten seconds for the medical report - 2026
animalrecords
research
We are collaborating on research with the Veterinary Faculty of Ludwig-Maximilians-University Munich and the Institute for Artificial Intelligence at Ravensburg-Weingarten University.
Artificial intelligence as a tool to aid in the differentiation of equine ophthalmic diseases with an emphasis on equine uveitis
Equine Veterinary Journal (EVJ)
May A., Gesell-May S., Müller T., Ertel W.
DOI: 10.1111/evj.13528
Abstract
Background
Due to recent developments in artificial intelligence, deep learning, and smart-device-technology, diagnostic software may be developed which can be executed offline as an app on smartphones using their high-resolution cameras and increasing processing power to directly analyse photos taken on the device.
Objectives
A software tool was developed to aid in the diagnosis of equine ophthalmic diseases, especially uveitis.
Study design
Prospective comparison of software and clinical diagnoses.
Methods
A deep learning approach for image classification was used to train software by analysing photographs of equine eyes to make a statement on whether the horse was displaying signs of uveitis or other ophthalmic diseases. Four basis networks of different sizes (MobileNetV2, InceptionV3, VGG16, VGG19) with modified top-layers were evaluated. Convolutional Neural Networks (CNN) were trained on 2346 pictures of equine eyes, which were augmented to 9384 images. 261 separate unmodified images were used to evaluate the performance of the trained network.
Results
Cross validation showed accuracy of 99.82% on training data and 96.66% on validation data when distinguishing between three categories (uveitis, other ophthalmic diseases, healthy).
Main limitations
One source of selection bias for the artificial intelligence presumably was the increased pupil size, which was mainly present in horses with ophthalmic diseases due to the use of mydriatics, and was not homogeneously dispersed in all categories of the dataset.
Conclusion
Our system for detection of equine uveitis is unique and novel and can differentiate between uveitis and other equine ophthalmic diseases. Its development also serves as a proof-of-concept for image-based detection of ophthalmic diseases in general and as a basis for its further use and expansion.
Artificial Intelligence for Lameness Detection in Horses — A Preliminary Study
Animals
Feuser A.-K., Gesell-May S., Müller T., May A.
DOI: 10.3390/ani12202804
Abstract
Background
Lameness in horses is a long-known issue influencing the welfare, as well as the use, of a horse. Nevertheless, the detection and classification of lameness mainly occurs on a subjective basis by the owner and the veterinarian.
Objectives
The aim of this study was the development of a lameness detection system based on pose estimation, which permits non-invasive and easily applicable gait analysis. The use of 58 reference points on easily detectable anatomical landmarks offers various possibilities for gait evaluation using a simple setup.
Study design
For this study, three groups of horses were used: one training group, one analysis group of fore and hindlimb lame horses and a control group of sound horses. The first group was used to train the network; afterwards, horses with and without lameness were evaluated.
Results
The results show that forelimb lameness can be detected by visualising the trajectories of the reference points on the head and both forelimbs. In hindlimb lameness, the stifle showed promising results as a reference point, whereas the tuber coxae were deemed unsuitable as a reference point.
Conclusion
The study presents a feasible application of pose estimation for lameness detection, but further development using a larger dataset is essential.
Comparison of veterinarians and a deep learning tool in the diagnosis of equine ophthalmic diseases
Equine Veterinary Journal (EVJ)
Scharre A., Scholler D., Gesell‐May S., Müller T., Zablotski Y., Ertel W., May A.
DOI: 10.1111/evj.14087
Abstract
Background / Objectives
The aim was to compare ophthalmic diagnoses made by veterinarians to a deep learning (artificial intelligence) software tool which was developed to aid in the diagnosis of equine ophthalmic diseases. As equine ophthalmology is a very specialised field in equine medicine, the tool may be able to help in diagnosing equine ophthalmic emergencies such as uveitis.
Study design
In silico tool development and assessment of diagnostic performance.
Methods
A deep learning tool which was developed and trained for classification of equine ophthalmic diseases was tested with 40 photographs displaying various equine ophthalmic diseases. The same data set was shown to different groups of veterinarians (equine, small animal, mixed practice, other) using an opinion poll to compare the results and evaluate the performance of the programme. Convolutional Neural Networks (CNN) were trained on 2346 photographs of equine eyes, which were augmented to 9384 images. Two hundred and sixty-one separate unmodified images were used to evaluate the trained network. The trained deep learning tool was used on 40 photographs of equine eyes (10 healthy, 12 uveitis, 18 other diseases). An opinion poll was used to evaluate the diagnostic performance of 148 veterinarians in comparison to the software tool.
Results
The probability for the correct answer was 93% for the AI programme. Equine veterinarians answered correctly in 76%, whereas other veterinarians reached 67% probability for the correct diagnosis.
Main limitations
Diagnosis was solely based on images of equine eyes without the possibility to evaluate the inner eye.
Conclusion
The deep learning tool proved to be at least equivalent to veterinarians in assessing ophthalmic diseases in photographs. We therefore conclude that the software tool may be useful in detecting potential emergency cases. In this context, blindness in horses may be prevented as the horse can receive accurate treatment or can be sent to an equine hospital. Furthermore, the tool gives less experienced veterinarians the opportunity to differentiate between uveitis and other ocular anterior segment disease and to support them in their decision-making regarding treatment.
animalrecords
references
animalrecords
Focus
Training Artificial Intelligences
Tobias Müller
- Management
- Business development
- Development of Artificial Intelligences
- Head of app development
Computer Science, Artificial Intelligence, Research
Focus
Integration of clinical data
Stefan Gesell-May
- Management
- Business development
- Head of Veterinary Medicine Content
- Member of the Expert Panel on Equine Ophthalmology
Veterinary practitioner